Microwave power amplifiers are important components in wireless telecommunication systems. A known problem with these amplifiers is that they can significantly distort the input signal, which typically is a carrier with modulation. This distortion causes errors in the telecommunication channels. An approach to eliminate or minimize amplifier distortion involves predistorting an input signal. According to this approach, a non-linear predistorter operator P[.] is applied to an input signal represented by a complex envelope A(t). The result is a predistorted signal P[A(t)]. The predistorted signal P[A(t)] is applied to the amplifier. P[.] is a perfect predistortion operator if the amplifier output is equal to a perfectly amplified version of A(t) when the amplifier receives P[A(t)] as the input signal. The predistorter distorts the input signal A(t) in such a way that it completely counteracts the nonlinear distortion of the amplifier.
Eliminating amplifier distortion is in many practical cases challenging because of the presence of long term memory effects. Long term memory effects can be attributed to dynamically changing internal biasing, temperature or trapping states. The distortion characteristics of an amplifier thus depend on the history of the input signal. This implies that the predistortion operator P[.] will not be a simple static map, but will be a dynamic operator that takes into account the history of the input signal A(t).
For an input signal X(t), an amplifier output signal Y(t) may be mathematically expressed using a functional operator F[.] as follows:Y(.)=F[X(.)]  (1).Symbol or terminology such as Y(.), X(.) and P[.] as variously used throughout this description, particularly the use of (.) and [.], signifies a generic representation of one or more arguments such as time, distance, temperature, etc. In equation (1), the operator F is not merely a function that maps X(t) into Y(t), but is rather a functional operator that maps a whole sequence of X(t) into Y(t).
Conventional predistorter algorithms have been based on discrete sampled versions of an input signal X(t), that may be expressed as follows:Xn=X(nTs)  (2).In equation (2), the subindex “n” denotes a sampled value at “n” times the sampling time Ts. Predistorter algorithms are typically based on a predistorted signal XPDn given as follows:XPDn=F(Xn,Xn-1,Xn-2, . . . ,β1,β2, . . . )  (3).In equation (3), F(.) is a multidimensional function parametrized by the set of coefficients β1, β2, . . . etc. The coefficients may take the form of polynomials, neural nets, and combinations and variations of different formats. The coefficients may be calculated based upon a test sequence, whereby a test input signal XT(t) is applied to an amplifier, and the corresponding output signal YT(t) is measured to derive the coefficients.
However, the function F(.) with a set of calculated constant coefficients fits as a good predistorter only for input signals that have the same spectral and statistical characteristics as the test sequence. A predistorter based on the above noted predistorter algorithm would be effective only for input signals that at least have the same modulation bandwidth and amplitude distribution as the test sequence. The performance of such a conventional predistorter will degrade quickly when the input signals do not have the same modulation bandwidth and amplitude distribution as the test sequence, such as when the mean power of the input signal is changed or when the input signal has a different type of modulation with amplitude distribution significantly different from the test sequence. The conventional predistorter as described is sensitive to amplitude distribution and bandwidth of the input signal.
Although the complexity of the function F(.) may be increased, the number of coefficients would consequently also increase. These coefficients cannot be directly measured and must instead be determined by a fitting procedure, which is very difficult to perform. As an alternative, new sets of coefficients may be continuously calculated using actually measured input signal sequences and corresponding output signal sequences as test sequences. However, this complex nonlinear feedback loop approach would necessitate many digital as well as analog parts, is very challenging to implement in an efficient and robust way, and a significant amount of time would be required to achieve accuracy responsive to a sudden change in the characteristics of the input signal.
What is needed is a method of predistorting signals for non-linear components in the presence of memory effects for a wide range of input signal modulation bandwidths and for all possible input signal amplitude distribution that is insensitive to input signal power level or modulation format, that is suitable for all possible peak-to-average ratios of input signals, that does not include feedback requiring continuous recalculation of predistorter coefficients, and that can be implemented using a feedforward approach.